app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
import matplotlib.pyplot as plt
import networkx as nx
import io
from PIL import Image
import torch
import os

print("Installation complete. Loading models...")

model_name = "csebuetnlp/mT5_multilingual_XLSum"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")
model = model.to(device)

question_generator = pipeline(
    "text2text-generation",
    model="valhalla/t5-small-e2e-qg",
    device=device if device == "cuda" else -1
)

def summarize_text(text, src_lang):
    inputs = tokenizer(text, return_tensors="pt", max_length=512, truncation=True).to(device)

    # Use more efficient generation parameters
    summary_ids = model.generate(
        inputs["input_ids"],
        max_length=150,
        min_length=30,
        length_penalty=2.0,
        num_beams=4,
        early_stopping=True
    )

    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return summary

def generate_questions(summary):
    questions = []
    for _ in range(3): 
        result = question_generator(
            summary,
            max_length=64,
            num_beams=4,
            do_sample=True,
            top_k=30,
            top_p=0.95,
            temperature=0.7
        )
        questions.append(result[0]['generated_text'])

    questions = list(set(questions))
    return questions

def generate_concept_map(summary, questions):
   
    G = nx.DiGraph()

    
    summary_short = summary[:50] + "..." if len(summary) > 50 else summary
    G.add_node("summary", label=summary_short)

    
    for i, question in enumerate(questions):
        q_short = question[:30] + "..." if len(question) > 30 else question
        node_id = f"Q{i}"
        G.add_node(node_id, label=q_short)
        G.add_edge("summary", node_id)

    
    plt.figure(figsize=(10, 8))
    pos = nx.spring_layout(G, seed=42)  
    nx.draw(G, pos, with_labels=False, node_color='skyblue',
            node_size=1500, arrows=True, connectionstyle='arc3,rad=0.1',
            edgecolors='black', linewidths=1)

    
    labels = nx.get_node_attributes(G, 'label')
    nx.draw_networkx_labels(G, pos, labels=labels, font_size=9,
                         font_family='sans-serif')

    buf = io.BytesIO()
    plt.savefig(buf, format='png', dpi=100, bbox_inches='tight')
    buf.seek(0)
    plt.close()

    return Image.open(buf)

def analyze_text(text, lang):
    if not text.strip():
        return "Please enter some text.", "No questions generated.", None

   
    try:
        print("Generating summary...")
        summary = summarize_text(text, lang)

        print("Generating questions...")
        questions = generate_questions(summary)

        print("Creating concept map...")
        concept_map_image = generate_concept_map(summary, questions)

        # Format questions as a list
        questions_text = "\n".join([f"- {q}" for q in questions])

        return summary, questions_text, concept_map_image
    except Exception as e:
        import traceback
        print(f"Error processing text: {str(e)}")
        print(traceback.format_exc())
        return f"Error processing text: {str(e)}", "", None

def generate_simple_concept_map(summary, questions):
    """Fallback concept map generator with minimal dependencies"""
    plt.figure(figsize=(10, 8))

    n_questions = len(questions)

    plt.scatter([0], [0], s=1000, color='skyblue', edgecolors='black')
    plt.text(0, 0, summary[:50] + "..." if len(summary) > 50 else summary,
             ha='center', va='center', fontsize=9)

    radius = 5
    for i, question in enumerate(questions):
        angle = 2 * 3.14159 * i / max(n_questions, 1)
        x = radius * 0.8 * -1 * (max(n_questions, 1) - 1) * ((i / max(n_questions - 1, 1)) - 0.5)
        y = radius * 0.6 * (i % 2 * 2 - 1)

        plt.scatter([x], [y], s=800, color='lightgreen', edgecolors='black')

        plt.plot([0, x], [0, y], 'k-', alpha=0.6)

        plt.text(x, y, question[:30] + "..." if len(question) > 30 else question,
                ha='center', va='center', fontsize=8)

    plt.axis('equal')
    plt.axis('off')

    buf = io.BytesIO()
    plt.savefig(buf, format='png', dpi=100, bbox_inches='tight')
    buf.seek(0)
    plt.close()

    return Image.open(buf)

examples = [
    ["الذكاء الاصطناعي هو فرع من علوم الكمبيوتر يهدف إلى إنشاء آلات ذكية تعمل وتتفاعل مثل البشر. بعض الأنشطة التي صممت أجهزة الكمبيوتر الذكية للقيام بها تشمل: التعرف على الصوت، التعلم، التخطيط، وحل المشاكل.", "ar"],
    ["Artificial intelligence is a branch of computer science that aims to create intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include: Speech recognition, learning, planning, and problem-solving.", "en"]
]

print("Creating Gradio interface...")

def analyze_text_with_fallback(text, lang):
    if not text.strip():
        return "Please enter some text.", "No questions generated.", None

    try:
        print("Generating summary...")
        summary = summarize_text(text, lang)

        print("Generating questions...")
        questions = generate_questions(summary)

        print("Creating concept map...")
        try:
            concept_map_image = generate_concept_map(summary, questions)
        except Exception as e:
            print(f"Main concept map failed: {e}, using fallback")
            concept_map_image = generate_simple_concept_map(summary, questions)

        questions_text = "\n".join([f"- {q}" for q in questions])

        return summary, questions_text, concept_map_image
    except Exception as e:
        import traceback
        print(f"Error processing text: {str(e)}")
        print(traceback.format_exc())
        return f"Error processing text: {str(e)}", "", None

iface = gr.Interface(
    fn=analyze_text_with_fallback, 
    inputs=[gr.Textbox(lines=10, placeholder="Enter text here..."), gr.Dropdown(["ar", "en"], label="Language")],
    outputs=[gr.Textbox(label="Summary"), gr.Textbox(label="Questions"), gr.Image(label="Concept Map")],
    examples=examples,
    title="AI Study Assistant",
    description="Enter a text in Arabic or English and the model will summarize it and generate questions and a concept map."
)

iface.launch(share=True)